Coronavirus US - Data Updates
davegoblue
11/8/2020
Background
This file is designed to analyze coronavirus data from a single state using three data sources:
- The COVID Tracking Project contains state-level data for cases, deaths, tests, and hospitalizations
- USA Facts contains county-level data for cases, deaths, and population
- CDC Weekly Deaths by Jurisdiction contains state-level data for total deaths by age cohort
Code for processing data from each of these sources is available in:
- Coronavirus_Statistics_CTP_v003
- Coronavirus_Statistics_USAF_v003
- Coronavirus_Statistics_CDC_v003
The goal of this file is to download updated data for the three main data sources, and to explore the performance of the segments as measured against the new data.
Functions are sourced and a variable mapping file is created:
# All functions assume that tidyverse and its components are loaded and available
# Other functions are declared in the sourcing files or use library::function()
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# If the same function is in both files, use the version from the more specific source
source("./Coronavirus_Statistics_Functions_Shared_v003.R")
source("./Coronavirus_Statistics_Functions_CTP_v003.R")
source("./Coronavirus_Statistics_Functions_USAF_v003.R")
source("./Coronavirus_Statistics_Functions_CDC_v003.R")
# Create a variable mapping file
varMapper <- c("cases"="Cases",
"newCases"="Increase in cases, most recent 30 days",
"casesroll7"="Rolling 7-day mean cases",
"deaths"="Deaths",
"newDeaths"="Increase in deaths, most recent 30 days",
"deathsroll7"="Rolling 7-day mean deaths",
"cpm"="Cases per million",
"cpm7"="Cases per day (7-day rolling mean) per million",
"newcpm"="Increase in cases, most recent 30 days, per million",
"dpm"="Deaths per million",
"dpm7"="Deaths per day (7-day rolling mean) per million",
"newdpm"="Increase in deaths, most recent 30 days, per million",
"hpm7"="Currently Hospitalized per million (7-day rolling mean)",
"tpm"="Tests per million",
"tpm7"="Tests per million per day (7-day rolling mean)",
"cdcExcess"="Excess all-cause (CDC)",
"ctp_death7"="COVID Tracking Project",
"usaf_death7"="USA Facts",
"CDC_deaths"="CDC total deaths",
"CDC_excess"="CDC excess deaths",
"CTP_cases"="COVID Tracking Project cases",
"CTP_deaths"="COVID Tracking Project deaths",
"CTP_hosp"="COVID Tracking Project hospitalized",
"CTP_tests"="COVID Tracking Project tests",
"USAF_cases"="USA Facts cases",
"USAF_deaths"="USA Facts deaths",
"vpm7"="Per million people (7-day rolling daily average)",
"vpm"="Per million people"
)Data Updates (early November)
New data from COVID Tracking Project are downloaded and assessed against the existing state-level segments:
# Use existing segments with updated data
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201108.csv"
test_old_201108 <- readRunCOVIDTrackingProject(thruLabel="Nov 7, 2020",
downloadTo=if (file.exists(locDownload)) NULL else locDownload,
readFrom=locDownload,
compareFile=readFromRDS("test_hier5_201025")$dfRaw,
useClusters=readFromRDS("test_hier5_201025")$useClusters
)##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## totalTestResultsSource = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## fips = col_character(),
## hash = col_character(),
## grade = col_logical()
## )
## i Use `spec()` for the full column specifications.## Warning: 7520 parsing failures.
## row col expected actual file
## 3 dateModified valid date 2020-11-07T24:00:00Z './RInputFiles/Coronavirus/CV_downloaded_201108.csv'
## 3 dateChecked valid date 2020-11-07T24:00:00Z './RInputFiles/Coronavirus/CV_downloaded_201108.csv'
## 4 dateModified valid date 2020-11-01T24:00:00Z './RInputFiles/Coronavirus/CV_downloaded_201108.csv'
## 4 dateChecked valid date 2020-11-01T24:00:00Z './RInputFiles/Coronavirus/CV_downloaded_201108.csv'
## 5 dateModified valid date 2020-11-07T24:00:00Z './RInputFiles/Coronavirus/CV_downloaded_201108.csv'
## ... ............ .......... .................... ....................................................
## See problems(...) for more details.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 9761373 229238 9362912
##
## *** COMPARISONS TO REFERENCE FILE: compareFile
##
## Checkin for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: states
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: dates
## In reference but not in current:
## In current but not in reference: 2020-11-07 2020-11-06 2020-11-05 2020-11-04 2020-11-03 2020-11-02 2020-11-01 2020-10-31 2020-10-30 2020-10-29 2020-10-28 2020-10-27 2020-10-26 2020-10-25
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("date", "name")## date name newValue oldValue
## 1 2020-03-07 positiveIncrease 171 176
## 2 2020-03-11 positiveIncrease 502 509
## 3 2020-03-13 positiveIncrease 1059 1072
## 4 2020-03-18 positiveIncrease 3023 3089
## 5 2020-03-26 positiveIncrease 17544 17720
## 6 2020-03-28 positiveIncrease 19586 19925
## 7 2020-03-29 positiveIncrease 19570 19348
## 8 2020-03-30 positiveIncrease 21691 22042
## 9 2020-04-01 positiveIncrease 26078 25791
## 10 2020-04-06 positiveIncrease 28592 29002
## 11 2020-04-13 positiveIncrease 24758 25195
## 12 2020-04-15 positiveIncrease 29755 30307
## 13 2020-04-16 positiveIncrease 31489 30978
## 14 2020-04-24 positiveIncrease 33698 34231
## 15 2020-05-12 positiveIncrease 22520 22890
## 16 2020-05-13 positiveIncrease 21577 21285
## 17 2020-05-15 positiveIncrease 25371 24685
## 18 2020-05-16 positiveIncrease 23560 24702
## 19 2020-05-17 positiveIncrease 20344 20009
## 20 2020-05-18 positiveIncrease 20812 21028
## 21 2020-05-23 positiveIncrease 22167 21531
## 22 2020-05-24 positiveIncrease 19148 20072
## 23 2020-05-30 positiveIncrease 23443 23682
## 24 2020-06-04 positiveIncrease 20256 20886
## 25 2020-06-05 positiveIncrease 23004 23394
## 26 2020-06-06 positiveIncrease 22773 23064
## 27 2020-06-10 positiveIncrease 20637 20894
## 28 2020-06-12 positiveIncrease 23185 23597
## 29 2020-06-18 positiveIncrease 27135 27746
## 30 2020-06-19 positiveIncrease 30881 31471
## 31 2020-06-21 positiveIncrease 28991 27928
## 32 2020-06-23 positiveIncrease 33848 33447
## 33 2020-07-02 positiveIncrease 53385 54085
## 34 2020-07-06 positiveIncrease 41416 41959
## 35 2020-08-01 positiveIncrease 60416 61101
## 36 2020-08-08 positiveIncrease 53158 53712
## 37 2020-08-14 positiveIncrease 57254 55636
## 38 2020-08-22 positiveIncrease 45722 46236
## 39 2020-09-02 positiveIncrease 30287 30603
## 40 2020-09-07 positiveIncrease 28237 28682
## 41 2020-09-15 positiveIncrease 34879 35445
## 42 2020-09-19 positiveIncrease 44886 45564
## 43 2020-09-20 positiveIncrease 35688 36295
## 44 2020-09-21 positiveIncrease 39062 39472
## 45 2020-09-24 positiveIncrease 43243 43772
## 46 2020-09-27 positiveIncrease 35061 35454
## 47 2020-09-28 positiveIncrease 36056 36524
## 48 2020-09-29 positiveIncrease 36289 36947
## 49 2020-10-21 positiveIncrease 60657 58606
## 50 2020-10-22 positiveIncrease 72887 75248## Joining, by = c("date", "name")## Warning: Removed 14 row(s) containing missing values (geom_path).##
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("state", "name")## state name newValue oldValue
## 1 FL positiveIncrease 766305 776249
## 2 PR positiveIncrease 31067 61275
## 3 RI positiveIncrease 30581 30116
## Rows: 13,999
## Columns: 55
## $ date <date> 2020-11-07, 2020-11-07, 2020-11-07, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 19306, 202482, 120828, 0, 257384, 95695...
## $ probableCases <dbl> NA, 30709, 10812, NA, 6590, NA, 7501, 4...
## $ negative <dbl> 728589, 1224595, 1308477, 1768, 1608041...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestResultsSource <chr> "totalTestsViral", "totalTestsViral", "...
## $ totalTestResults <dbl> 747288, 1396368, 1418493, 1768, 1858835...
## $ hospitalizedCurrently <dbl> 105, 1015, 633, NA, 1139, 3456, 1041, 4...
## $ hospitalizedCumulative <dbl> NA, 21294, 7415, NA, 22170, NA, 9911, 1...
## $ inIcuCurrently <dbl> NA, NA, 235, NA, 249, 901, NA, NA, 25, ...
## $ inIcuCumulative <dbl> NA, 2121, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 8, NA, 105, NA, 137, NA, NA, NA, 14, NA...
## $ onVentilatorCumulative <dbl> NA, 1225, 865, NA, NA, NA, NA, NA, NA, ...
## $ recovered <dbl> 7157, 84471, 106594, NA, 42950, NA, 847...
## $ dataQualityGrade <chr> "A", "A", "A+", "D", "A+", "B", "A", "B...
## $ lastUpdateEt <chr> "11/7/2020 03:59", "11/7/2020 11:00", "...
## $ dateModified <dttm> 2020-11-07 03:59:00, 2020-11-07 11:00:...
## $ checkTimeEt <chr> "11/06 22:59", "11/07 06:00", "11/06 19...
## $ death <dbl> 84, 3082, 2068, 0, 6147, 17939, 2168, 4...
## $ hospitalized <dbl> NA, 21294, 7415, NA, 22170, NA, 9911, 1...
## $ dateChecked <dttm> 2020-11-07 03:59:00, 2020-11-07 11:00:...
## $ totalTestsViral <dbl> 747288, 1396368, 1418493, 1768, NA, 195...
## $ positiveTestsViral <dbl> 21064, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ negativeTestsViral <dbl> 725850, NA, 1308477, NA, NA, NA, NA, NA...
## $ positiveCasesViral <dbl> 19306, 171773, 110016, 0, 250794, 95695...
## $ deathConfirmed <dbl> 84, 2864, 1890, NA, 5730, NA, NA, 3757,...
## $ deathProbable <dbl> NA, 218, 178, NA, 417, NA, NA, 914, NA,...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 2177091, NA, 55...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, NA, 1858835, NA, 1303345, N...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 324293, NA, 187631, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 14007, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 173624, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, 66104, NA, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, 76277, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, 12392, NA, NA, NA, NA, NA, NA, ...
## $ totalTestsAntigen <dbl> NA, NA, 21856, NA, NA, NA, NA, 26512, N...
## $ positiveTestsAntigen <dbl> NA, NA, 3300, NA, NA, NA, NA, NA, NA, N...
## $ fips <chr> "02", "01", "05", "60", "04", "06", "08...
## $ positiveIncrease <dbl> 607, 1768, 1598, 0, 2620, 5863, 3463, 0...
## $ negativeIncrease <dbl> -34013, 7593, 10213, 0, 14060, 162939, ...
## $ total <dbl> 747895, 1427077, 1429305, 1768, 1865425...
## $ totalTestResultsIncrease <dbl> -34013, 8920, 11491, 0, 16524, 168802, ...
## $ posNeg <dbl> 747895, 1427077, 1429305, 1768, 1865425...
## $ deathIncrease <dbl> 0, 33, 12, 0, 38, 73, 10, 0, 2, 0, 87, ...
## $ hospitalizedIncrease <dbl> 0, 0, 14, 0, 152, 0, 197, 0, 0, 0, 161,...
## $ hash <chr> "f2176e93601204643e0618a661e7c3603f44f4...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 44159 976 NA 457982 1185
## 2 TRUE 9717214 228262 NA 154892254 12814
## Rows: 12,814
## Columns: 6
## $ date <date> 2020-11-07, 2020-11-07, 2020-11-07, 2020-11-07, 2020-11-07,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 607, 1768, 1598, 2620, 5863, 3463, 0, 99, 223, 4380, 1719, 1...
## $ deaths <dbl> 0, 33, 12, 38, 73, 10, 0, 2, 0, 87, 39, 0, 11, 8, 91, 45, 0,...
## $ hosp <dbl> 105, 1015, 633, 1139, 3456, 1041, 402, 77, 115, 2672, 1859, ...
## $ tests <dbl> -34013, 8920, 11491, 16524, 168802, 35158, 0, 4262, 5159, 48...
## Rows: 12,814
## Columns: 14
## $ date <date> 2020-01-22, 2020-01-22, 2020-01-23, 2020-01-23, 2020-01-24,...
## $ state <chr> "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...
## $ cpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.1471796, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA...
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, NA, NA, NA, 0.04205130, 0.00000000, 0.06307695, ...## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
##
## Recency is defined as 2020-10-09 through current
##
## Recency is defined as 2020-10-09 through current
## `summarise()` regrouping output by 'state', 'cluster', 'date' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
saveToRDS(test_old_201108, ovrWriteError=FALSE)Cases appear to be spiking in some of the states in the segment that had previously been less impacted. Hospitalizations in aggregate in this segment are starting to slope upwards, though not yet at the same rate as the increase in cases.
New data from USA Facts are downloaded and assessed against the existing county-level segments:
# Locations for the population, case, and death file
popLoc <- "./RInputFiles/Coronavirus/covid_county_population_usafacts.csv"
caseLoc <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20201109.csv"
deathLoc <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201109.csv"
# Run old segments against new data
cty_old_20201109 <- readRunUSAFacts(maxDate="2020-11-07",
popLoc=popLoc,
caseLoc=caseLoc,
deathLoc=deathLoc,
dlCaseDeath=!(file.exists(caseLoc) & file.exists(deathLoc)),
oldFile=readFromRDS("cty_20201026")$dfBurden,
existingCountyClusters=readFromRDS("cty_20201026")$clustVec
)##
## -- Column specification --------------------------------------------------------
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Rows: 929,745
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## i Use `spec()` for the full column specifications.## Warning: 1 parsing failure.
## row col expected actual file
## 1366 11/7/20 no trailing characters 1,020 './RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201109.csv'## Rows: 929,745
## Columns: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Warning: `expand_scale()` is deprecated; use `expansion()` instead.## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).## `summarise()` ungrouping output (override with `.groups` argument)
## Warning: Removed 2 rows containing missing values (geom_point).## `summarise()` ungrouping output (override with `.groups` argument)##
## Shapes will be created without any floor on the number of cases per million
## Shapes will be created without any floor on the number of deaths per million
## *** Counties with 0 cases/deaths or that fall below the floor for minCase/minDeath ***
## # A tibble: 1 x 4
## cpm_mean_is0 dpm_mean_is0 dpm_mean_ltDeath cpm_mean_ltCase
## <dbl> <dbl> <dbl> <dbl>
## 1 0 NA NA 0## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'date', 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 rows containing missing values (geom_text).
##
## Recency is defined as 2020-10-09 through current
##
## Recency is defined as 2020-10-09 through current## Warning: Removed 2 rows containing missing values (geom_point).## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)
## Warning: `expand_scale()` is deprecated; use `expansion()` instead.
## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
saveToRDS(cty_old_20201109, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/cty_old_20201109.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLCases appear to be growing in many of the county clusters, though deaths per million per day remain well below the peaks observed in April in the earlier-hit counties.
Next, all-cause death data from the CDC are downloaded and assessed:
# Use data that have previously been downloaded
cdcLoc <- "Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20201110.csv"
cdcList_20201110 <- readRunCDCAllCause(loc=cdcLoc,
startYear=2015,
curYear=2020,
weekThru=36,
startWeek=9,
lst=readFromRDS("test_old_201108"),
epiMap=readFromRDS("epiMonth"),
agePopData=readFromRDS("usPopBucket2020"),
cvDeathThru="2020-09-05",
cdcPlotStartWeek=10,
dlData=!file.exists(paste0("./RInputFiles/Coronavirus/", cdcLoc))
)## Rows: 178,482
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "A...
## $ `Week Ending Date` <chr> "01/10/2015", "01/17/2015", "01/24/2015", "01/...
## $ `State Abbreviation` <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL"...
## $ Year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015...
## $ Week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...
## $ `Age Group` <chr> "25-44 years", "25-44 years", "25-44 years", "...
## $ `Number of Deaths` <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50...
## $ `Time Period` <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2...
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)"...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Rows: 178,482
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <chr> "25-44 years", "25-44 years", "25-44 years", "25-44 ye...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2019", "2...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Check Control Levels and Record Counts for Renamed Data:## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <chr> <int> <int> <dbl>
## 1 25-44 years 26725 5 3266337
## 2 45-64 years 32640 10 12792834
## 3 65-74 years 32632 12 12691956
## 4 75-84 years 32652 14 15776825
## 5 85 years and older 32640 15 20567595
## 6 Under 25 years 21193 0 1407550## `summarise()` regrouping output by 'period', 'year' (override with `.groups` argument)## # A tibble: 12 x 6
## # Groups: period, year [6]
## period year type n n_deaths_na deaths
## <chr> <int> <chr> <int> <int> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 15285 0 5416393
## 2 2015-2019 2015 Unweighted 15285 0 5416393
## 3 2015-2019 2016 Predicted (weighted) 15365 0 5483764
## 4 2015-2019 2016 Unweighted 15365 0 5483764
## 5 2015-2019 2017 Predicted (weighted) 15317 0 5643342
## 6 2015-2019 2017 Unweighted 15317 0 5643342
## 7 2015-2019 2018 Predicted (weighted) 15305 0 5698005
## 8 2015-2019 2018 Unweighted 15305 0 5698005
## 9 2015-2019 2019 Predicted (weighted) 15319 0 5725544
## 10 2015-2019 2019 Unweighted 15319 0 5725544
## 11 2020 2020 Predicted (weighted) 12677 34 5332986
## 12 2020 2020 Unweighted 12623 22 5236015## `summarise()` regrouping output by 'period' (override with `.groups` argument)## # A tibble: 3 x 5
## # Groups: period [2]
## period Suppress n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-2019 <NA> 153182 0 5.59e7
## 2 2020 Suppressed (counts highly incomplete, <5~ 56 56 0.
## 3 2020 <NA> 25244 0 1.06e7## `summarise()` regrouping output by 'period' (override with `.groups` argument)## # A tibble: 9 x 5
## # Groups: period [2]
## period Note n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-20~ <NA> 153182 0 5.59e7
## 2 2020 Data in recent weeks are incomplete. Only~ 19873 10 8.61e6
## 3 2020 Data in recent weeks are incomplete. Only~ 456 0 2.12e5
## 4 2020 Data in recent weeks are incomplete. Only~ 384 35 4.69e4
## 5 2020 Data in recent weeks are incomplete. Only~ 2213 11 7.28e5
## 6 2020 Data in recent weeks are incomplete. Only~ 12 0 7.12e3
## 7 2020 Estimates for Pennsylvania are too low fo~ 48 0 2.26e4
## 8 2020 Weights may be too low to account for und~ 328 0 1.25e5
## 9 2020 <NA> 1986 0 8.13e5## `summarise()` regrouping output by 'state' (override with `.groups` argument)## state Jurisdiction n n_deaths_na deaths
## 1 US United States 3636 0 33131804
## 2 CA California 3636 0 3152950
## 3 FL Florida 3636 0 2423089
## 4 TX Texas 3636 0 2383305
## 5 PA Pennsylvania 3636 0 1589393
## 6 OH Ohio 3636 0 1432144
## 7 IL Illinois 3636 0 1250417
## 8 NY New York 3636 0 1183446
## 9 MI Michigan 3636 0 1140202
## 10 NC North Carolina 3568 28 1066679
## 11 GA Georgia 3636 0 994220
## 12 NJ New Jersey 3630 0 886576
## 13 TN Tennessee 3636 0 865673
## 14 VA Virginia 3636 0 795842
## 15 IN Indiana 3632 0 769795
## 16 MO Missouri 3633 0 749309
## 17 AZ Arizona 3636 0 702644
## 18 MA Massachusetts 3602 0 701039
## 19 YC New York City 3632 0 685046
## 20 WA Washington 3636 0 662228
## 21 AL Alabama 3635 0 615046
## 22 WI Wisconsin 3618 0 608567
## 23 MD Maryland 3630 0 585192
## 24 SC South Carolina 3634 0 576703
## 25 KY Kentucky 3599 0 560150
## 26 LA Louisiana 3628 0 541388
## 27 MN Minnesota 3592 0 517087
## 28 CO Colorado 3634 0 458526
## 29 OK Oklahoma 3622 9 455633
## 30 OR Oregon 3466 0 424398
## 31 MS Mississippi 3574 0 374274
## 32 AR Arkansas 3532 0 372266
## 33 CT Connecticut 3191 14 365303
## 34 IA Iowa 3271 0 349432
## 35 PR Puerto Rico 3347 0 340685
## 36 KS Kansas 3327 0 304875
## 37 NV Nevada 3372 0 297386
## 38 WV West Virginia 3075 2 257638
## 39 UT Utah 3522 0 219851
## 40 NM New Mexico 3210 0 210537
## 41 NE Nebraska 2920 0 193764
## 42 ME Maine 2712 0 164439
## 43 ID Idaho 2834 0 157266
## 44 NH New Hampshire 2734 0 138417
## 45 HI Hawaii 2625 0 127727
## 46 RI Rhode Island 2534 3 116993
## 47 MT Montana 2618 0 113028
## 48 DE Delaware 2627 0 102300
## 49 SD South Dakota 2510 0 89238
## 50 ND North Dakota 2496 0 77815
## 51 DC District of Columbia 2609 0 65724
## 52 VT Vermont 2392 0 63461
## 53 WY Wyoming 2379 0 48740
## 54 AK Alaska 2412 0 43447## `summarise()` regrouping output by 'year' (override with `.groups` argument)## Rows: 178,482
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Rows: 87,264
## Columns: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
##
## *** Data suppression checks ***
## # A tibble: 5 x 11
## Jurisdiction weekEnding state year week age deaths period type Suppress
## <chr> <date> <chr> <fct> <int> <fct> <dbl> <fct> <chr> <chr>
## 1 North Carol~ 2020-09-05 NC 2020 36 25-4~ NA 2020 Pred~ Suppres~
## 2 North Carol~ 2020-09-05 NC 2020 36 45-6~ NA 2020 Pred~ Suppres~
## 3 North Carol~ 2020-09-05 NC 2020 36 65-7~ NA 2020 Pred~ Suppres~
## 4 North Carol~ 2020-09-05 NC 2020 36 75-8~ NA 2020 Pred~ Suppres~
## 5 North Carol~ 2020-09-05 NC 2020 36 85 y~ NA 2020 Pred~ Suppres~
## # ... with 1 more variable: Note <chr>
##
## Data suppression checks OK - 5 records in current week/year suppressed## `summarise()` regrouping output by 'Jurisdiction', 'weekEnding', 'state', 'year', 'week', 'age', 'period', 'type' (override with `.groups` argument)## Rows: 82,069
## Columns: 12
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, ...
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years,...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## First duplicate is in row number (0 means no duplicates): 0## `summarise()` ungrouping output (override with `.groups` argument)## `summarise()` regrouping output by 'cluster' (override with `.groups` argument)## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'week' (override with `.groups` argument)
## `summarise()` regrouping output by 'year', 'week' (override with `.groups` argument)
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## `summarise()` regrouping output by 'year' (override with `.groups` argument)## `summarise()` regrouping output by 'state', 'quarter', 'month' (override with `.groups` argument)## `summarise()` regrouping output by 'state' (override with `.groups` argument)
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## `summarise()` ungrouping output (override with `.groups` argument)## Joining, by = "state"
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## `summarise()` ungrouping output (override with `.groups` argument)
saveToRDS(cdcList_20201110, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/cdcList_20201110.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLThe death data can then be combined and analyzed, using the function from Coronavirus_Statistics_States_v003:
combineDeathData <- function(ctp,
usaf,
cdc,
keyState,
curYear=2020,
minDate=as.Date(paste0(curYear, "-01-01")),
perMillion=FALSE,
glimpseIntermediate=FALSE,
facetFreeY=!perMillion,
returnData=TRUE
) {
# FUNCTION ARGUMENTS:
# ctp: the list with COVID Tracking Project data
# usaf: the data frame with USA Facts data
# cdc: the list with CDC data
# keyState: the state(s) to be explored
# curYear: current year
# minDate: the minimum date to use in the CDC data
# perMillion: boolean, should data be show on a per-million-people basis?
# glimpseIntermediate: boolean, should glimpses of frames be provided as they are built?
# facetFreeY: boolean, should facets be created with free_y scales (only relevant if 2+ keyStates)
# returnData: boolean, should the data frame be returned?
# STEP 0a: Extract relevant elements from lists (use frame as-is if directly passed)
if ("list" %in% class(ctp)) ctp <- ctp[["consolidatedPlotData"]]
if ("list" %in% class(usaf)) usaf <- usaf[["clusterStateData"]]
if ("list" %in% class(cdc)) cdc <- cdc[["stateAgg"]]
# STEP 0b: Create a mapping file of date to epiWeek
epiMap <- tibble::tibble(date=seq.Date(from=minDate, to=as.Date(paste0(curYear, "-12-31")), by="1 day"),
week=lubridate::epiweek(date)
)
# STEP 1: Filter to only relevant data
# STEP 1a: COVID Tracking Project
ctp <- ctp %>%
ungroup() %>%
filter(name=="deaths", state %in% keyState)
if(glimpseIntermediate) glimpse(ctp)
# STEP 1b: USA Facts
usaf <- usaf %>%
ungroup() %>%
filter(state %in% keyState)
if(glimpseIntermediate) glimpse(usaf)
# STEP 1c: CDC
cdc <- cdc %>%
ungroup() %>%
filter(year==curYear, state %in% keyState)
if(glimpseIntermediate) glimpse(cdc)
# STEP 2a: Sum the county-level data so that it is state-level data
usafState <- usaf %>%
group_by(state, date) %>%
summarize(deaths=sum(deaths), dpm7=sum(dpm7*pop)/sum(pop), pop=sum(pop), .groups="drop_last") %>%
ungroup()
# STEP 2b: Convert the CDC data to an estimated daily total (split the weekly total evenly)
cdcDaily <- cdc %>%
left_join(epiMap, by=c("week")) %>%
select(state, week, date, cdcDeaths=deaths, cdcExcess=delta) %>%
mutate(cdcDeaths=cdcDeaths/7, cdcExcess=cdcExcess/7)
# STEP 3: Create a state death-level database by date
dailyDeath <- select(ctp, state, date, ctpDeaths=value, ctp_dpm7=vpm7, ctp_pop=pop) %>%
full_join(select(usafState, state, date, usafDeaths=deaths, usaf_dpm7=dpm7, usaf_pop=pop),
by=c("state", "date")
) %>%
full_join(cdcDaily, by=c("state", "date")) %>%
arrange(state, date) %>%
mutate(ctp_death7=ctp_dpm7*ctp_pop/1000000, usaf_death7=usaf_dpm7*usaf_pop/1000000)
if(glimpseIntermediate) glimpse(dailyDeath)
# STEP 4a: Assign a population by state
statePop <- dailyDeath %>%
group_by(state) %>%
summarize(pop=max(usaf_pop, ctp_pop, na.rm=TRUE), .groups="drop_last")
# STEP 4b: Plot the deaths data
p1 <- dailyDeath %>%
select(state, date, ctp_death7, usaf_death7, cdcExcess) %>%
pivot_longer(-c(state, date), names_to="source", values_to="deaths") %>%
filter(!is.na(deaths)) %>%
left_join(statePop, by="state") %>%
ggplot(aes(x=date, y=deaths*if(perMillion) (1000000/pop) else 1)) +
geom_line(aes(group=source, color=varMapper[source])) +
labs(x="",
y=paste0("Deaths", if(perMillion) " per million" else ""),
title=paste0(curYear, " deaths per day in ", paste0(keyState, collapse=", ")),
subtitle=paste0("Rolling 7-day average", if(perMillion) " per million people" else ""),
caption="CDC estimated excess all-cause deaths, weekly total divided by 7 to estimate daily total"
) +
scale_x_date(date_breaks="1 month", date_labels="%b") +
scale_color_discrete("Data source") +
theme(legend.position="bottom") +
geom_hline(yintercept=0, lty=2)
if (length(keyState) > 1) p1 <- p1 + facet_wrap(~state, scales=if(facetFreeY) "free_y" else "fixed")
print(p1)
# STEP 5: Return the daily death file
if(returnData) dailyDeath
}
# Example function
combineDeathData(ctp=test_old_201108,
usaf=cty_old_20201109$clusterStateData,
cdc=cdcList_20201110,
keyState=c("NY", "NJ", "MA", "FL", "GA", "TX", "AZ", "MS", "LA", "MI", "IL", "WI"),
perMillion=FALSE,
returnData=FALSE
)
combineDeathData(ctp=test_old_201108,
usaf=cty_old_20201109$clusterStateData,
cdc=cdcList_20201110,
keyState=c("NY", "NJ", "MA", "FL", "GA", "TX", "AZ", "MS", "LA", "MI", "IL", "WI"),
perMillion=TRUE,
returnData=FALSE
)
In general, the shapes of the curves are well aligned, though the CDC excess-deaths generally start earlier and peak higher than the coronavirus deaths curves from COVID Tracking Project and USA Facts. In general, the northeastern states hit early have a classic epidemic peak curve, while the states hit later tend to have lower peaks (per million) but, in some cases, a much extended timeline of non-zero disease impact.
Next, an integrated state data file is created from the latest data:
ctpList <- readFromRDS("test_old_201108")
usafData <- readFromRDS("cty_old_20201109")$clusterStateData
cdcList <- readFromRDS("cdcList_20201110")
# Function to convert a COVID Tracking Project file for further processing
prepCTPData <- function(ctp) {
# FUNCTION AGRUMENTS:
# ctp: a properly formatted list or data frame containing processed COVID Tracking Project data
# Pull the relevant data frame if a list has been passed
if ("list" %in% class(ctp)) ctp <- ctp[["consolidatedPlotData"]]
# Ungroup the data, delete the state named 'cluster', and Create a value7 metric
ctp <- ctp %>%
ungroup() %>%
filter(state != "cluster") %>%
mutate(value7=ifelse(is.na(vpm7), NA, vpm7*pop/1000000))
# Split state-cluster-population as a separate file unique by state
ctpDemo <- ctp %>%
group_by(state, cluster) %>%
summarize(pop=max(pop, na.rm=TRUE), .groups="drop_last") %>%
ungroup()
# Create a final data file with the key elements
ctpData <- ctp %>%
rename(metric=name) %>%
mutate(source="CTP", name=paste0(source, "_", metric)) %>%
select(state, date, metric, source, name, value, value7, vpm, vpm7)
# Return the key data frames
list(ctpDemo=ctpDemo, ctpData=ctpData)
}
ctpPrepped <- prepCTPData(ctpList)
# Function to convert a USA Facts file for further processing
prepUSAFData <- function(usaf) {
# FUNCTION AGRUMENTS:
# usaf: a properly formatted list or data frame containing processed USA Facts data
# Pull the relevant data frame if a list has been passed
if ("list" %in% class(usaf)) usaf <- usaf[["clusterStateData"]]
# Sum the data to state, keeping only state-date-pop-cases-deaths, then pivot longer
usaf <- usaf %>%
group_by(state, date) %>%
summarize(cases=sum(cases), deaths=sum(deaths), pop=sum(pop), .groups="drop_last") %>%
ungroup() %>%
pivot_longer(-c(state, date, pop), names_to="metric", values_to="value")
# Create the rolling-7 for value, having grouped by state-pop-metric and sorted by date
# Add the per million component
usaf <- usaf %>%
group_by(state, pop, metric) %>%
arrange(date) %>%
helperRollingAgg(origVar="value", newName="value7") %>%
ungroup() %>%
mutate(vpm=value*1000000/pop, vpm7=value7*1000000/pop)
# Split state-pop as a separate file unique by state
usafDemo <- usaf %>%
group_by(state) %>%
summarize(pop=max(pop, na.rm=TRUE), .groups="drop_last") %>%
ungroup()
# Create a final data file with the key elements
usafData <- usaf %>%
mutate(source="USAF", name=paste0(source, "_", metric)) %>%
select(state, date, metric, source, name, value, value7, vpm, vpm7)
# Return the key data frames
list(usafDemo=usafDemo, usafData=usafData)
}
usafPrepped <- prepUSAFData(usafData)
# Function to convert a CDC file for further processing
prepCDCData <- function(cdc,
popData,
startYear=2020,
startDate=as.Date(paste0(startYear, "-01-01")),
endDate=as.Date(paste0(startYear, "-12-31"))
) {
# FUNCTION AGRUMENTS:
# cdc: a properly formatted list or data frame containing processed CDC data
# popData: a file containing fields state-pop
# startYear: starting year (CDC data will be filtered for this year and later)
# startDate: the starting date for use in the mapping file to create daily estimates
# endDate: the ending date for use in the mapping file to create daily estimates
# Pull the relevant data frame if a list has been passed
if ("list" %in% class(cdc)) cdc <- cdc[["stateAgg"]]
# Create a mapping file of dates to epiweek-epiyear
epiMap <- tibble::tibble(date=seq.Date(from=startDate, to=endDate, by="1 day"),
year=lubridate::epiyear(date),
week=lubridate::epiweek(date)
)
# Filter the data to the relevant year and keep state-year-week-deaths-excess
cdc <- cdc %>%
filter(yearint >= startYear) %>%
select(state, yearint, week, deaths, excess=delta)
# Merge in the daily mapping file, divide all totals by 7 to reflect weekly to daily, and pivot longer
cdc <- cdc %>%
left_join(epiMap, by=c("yearint"="year", "week"="week")) %>%
mutate(deaths=deaths/7, excess=excess/7) %>%
select(state, date, deaths, excess) %>%
pivot_longer(-c(state, date), names_to="metric", values_to="value")
# Create the rolling-7 for value, having grouped by state-metric and sorted by date
# Add the per million component
cdc <- cdc %>%
group_by(state, metric) %>%
arrange(date) %>%
helperRollingAgg(origVar="value", newName="value7") %>%
ungroup() %>%
left_join(select(popData, state, pop), by="state") %>%
mutate(vpm=value*1000000/pop,
vpm7=value7*1000000/pop,
source="CDC",
name=paste0(source, "_", metric)
) %>%
select(state, date, pop, metric, source, name, value, value7, vpm, vpm7)
# Return the key data frame as a list
list(cdcDemo=select(cdc, state, pop), cdcData=select(cdc, -pop))
}
# Create an integrated state demographics file
demoData <- ctpPrepped$ctpDemo %>%
rename(popCTP=pop) %>%
full_join(rename(usafPrepped$usafDemo, popUSAF=pop), by="state") %>%
mutate(pop=pmax(popCTP, popUSAF))
cdcPrepped <- prepCDCData(cdcList, popData=demoData)
# Integrated state data
stateData <- ctpPrepped$ctpData %>%
bind_rows(usafPrepped$usafData) %>%
bind_rows(cdcPrepped$cdcData)
glimpse(stateData)## Rows: 104,103
## Columns: 9
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", ...
## $ date <date> 2020-03-06, 2020-03-07, 2020-03-08, 2020-03-09, 2020-03-10,...
## $ metric <chr> "cases", "cases", "cases", "cases", "cases", "cases", "cases...
## $ source <chr> "CTP", "CTP", "CTP", "CTP", "CTP", "CTP", "CTP", "CTP", "CTP...
## $ name <chr> "CTP_cases", "CTP_cases", "CTP_cases", "CTP_cases", "CTP_cas...
## $ value <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 3, 0, 6, 2, 8, 0, 14, 6,...
## $ value7 <dbl> NA, NA, NA, 0.0000000, 0.1428571, 0.1428571, 0.1428571, 0.14...
## $ vpm <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, ...
## $ vpm7 <dbl> NA, NA, NA, 0.0000000, 0.1934601, 0.1934601, 0.1934601, 0.19...# Control totals
stateData %>%
group_by(name) %>%
summarize(value=sum(value, na.rm=TRUE), value7=sum(value7, na.rm=TRUE), .groups="drop_last")## # A tibble: 8 x 3
## name value value7
## <chr> <dbl> <dbl>
## 1 CDC_deaths 2224832. 2173735.
## 2 CDC_excess 242466. 240886.
## 3 CTP_cases 9717214 9372412.
## 4 CTP_deaths 228262 224869.
## 5 CTP_hosp 9301372 9036822.
## 6 CTP_tests 154892254 150735326.
## 7 USAF_cases 9679664 9336904.
## 8 USAF_deaths 233286 230182.The alignment of cases and deaths data can then be plotted:
plotStateMetric <- function(df,
yVal,
namesPlot,
keyStates,
namesSec=NULL,
scaleSec=NULL,
plotTitle=NULL,
plotSub=NULL,
plotCaption=NULL,
primYLab=NULL,
secYLab="Caution, different metric and scale",
facetFixed=TRUE,
mapper=varMapper,
combStates=vector("character", 0),
popData=NULL,
yValPerCap=(yVal %in% c("vpm", "vpm7")),
printPlot=TRUE,
returnData=FALSE
) {
# FUNCTION ARGUMENTS:
# df: data frame with integrated state data
# yVal: column to use for the yValues
# namesPlot: the values of column 'name' to be kept and plotted
# keyStates: states to be included
# if more than one state is passed, facets will be created
# namesSec: names to be plotted on a secondary y-axes
# scaleSec: scale to be used for the secondary axis
# namesSec/scaleSec should be similar in magnitude to namesPlot
# plotTitle: plot title to be used (NULL means none)
# plotSub: plot subtitle to be used (NULL means none)
# plotCaption: plot caption to be used (NULL means none)
# primYLab: primary y label (NULL means use mapper)
# secYLab: secondary y label (default is "Caution, different metric and scale")
# facetFixed: boolean, if TRUE scales="fixed", if FALSE scales="free_y"
# only relevant if length(keyStates) > 1
# mapper: mapping file for variable names to descriptive names
# combStates: states that should be combined together for plotting (named vector, c("state"="newName"))
# popData: a population file for combining states
# yValPerCap: boolean, is the y-value of type per-capita?
# printPlot: boolean, whether to print the plots
# returnData: boolean, whether to return the data
# Routine is only set up for a secondary axis with facetFixed=TRUE
if (!is.null(namesSec) & !facetFixed) stop("\nSecondary axis only programmed for scales='fixed'\n")
# Include variables in namesSec as part of namesPlot so they are kept by filter
if (!is.null(namesSec)) namesPlot <- unique(c(namesPlot, namesSec))
# Filter the data for only the key elements
df <- df %>%
select_at(vars(all_of(c("state", "date", "name", yVal)))) %>%
filter(state %in% keyStates, name %in% namesPlot)
# If there is a list of states to combine, process them
if (length(combStates) > 0) {
if (is.null(popData)) { stop("\nCombining states requires population data\n") }
# Create a data frame with population and new state names
df <- df %>%
left_join(select(popData, state, pop), by="state") %>%
mutate(state=ifelse(state %in% names(combStates), combStates[state], state))
# Aggregate to the new 'state' level data
if (yValPerCap) {
df <- df %>%
group_by(state, date, name) %>%
summarize(!!yVal:=sum(get(yVal)*pop)/sum(pop), pop=sum(pop), .groups="drop_last")
} else {
df <- df %>%
group_by(state, date, name) %>%
summarize(!!yVal:=sum(get(yVal)), pop=sum(pop), .groups="drop_last")
}
# Ungroup data frame
df <- df %>%
ungroup()
}
# If there is a secondary scale but no scaleSec has been passed, create one
if (!is.null(namesSec) & is.null(scaleSec)) {
maxPrimary <- df %>%
filter(name %in% setdiff(namesPlot, namesSec)) %>%
summarize(max(get(yVal), na.rm=TRUE), .groups="drop_last") %>%
max()
maxSecondary <- df %>%
filter(name %in% namesSec) %>%
summarize(max(get(yVal), na.rm=TRUE), .groups="drop_last") %>%
max()
scaleSec <- maxSecondary/maxPrimary
cat("\nWill scale by:", scaleSec, "\n")
}
# Create the primary y-axis label from mapper if it has not been passed
if (is.null(primYLab)) primYLab <- mapper[yVal]
# Create the relevant line plot
if (printPlot) {
p1 <- df %>%
filter(!is.na(get(yVal))) %>%
ggplot(aes_string(x="date")) +
geom_line(data=~filter(., name %in% setdiff(namesPlot, namesSec)),
aes(y=get(yVal), group=name, color=mapper[name])
) +
scale_x_date(date_breaks="1 month", date_labels="%b") +
geom_hline(aes(yintercept=0), lty=2) +
labs(x="") +
theme(axis.text.x = element_text(angle = 90))
if (!is.null(namesSec)) {
p1 <- p1 +
geom_line(data=~filter(., name %in% namesSec),
aes(y=get(yVal)/scaleSec, color=mapper[name], group=name)
) +
scale_y_continuous(name=primYLab,
sec.axis=sec_axis(~.*scaleSec, name=secYLab)
)
} else {
p1 <- p1 + scale_y_continuous(name=primYLab)
}
if (length(keyStates) > 1) p1 <- p1 + facet_wrap(~state, scales=if(facetFixed) "fixed" else "free_y")
if (!is.null(plotTitle)) p1 <- p1 + labs(title=plotTitle)
if (!is.null(plotSub)) p1 <- p1 + labs(subtitle=plotSub)
if (!is.null(plotCaption)) p1 <- p1 + labs(caption=plotCaption)
p1 <- p1 + scale_color_discrete("Source and metric")
print(p1)
}
if (returnData) return(df)
}
# Example of combining states
ne_casedeath <- plotStateMetric(stateData,
yVal="vpm7",
namesPlot=c("CTP_cases"),
namesSec=c("CTP_deaths"),
keyStates=c("NY", "NJ", "MA", "CT", "RI", "NH", "VT", "ME"),
combStates=c("MA"="S NE", "CT"="S NE", "RI"="S NE",
"NH"="N NE", "VT"="N NE", "ME"="N NE"
),
plotTitle="2020 coronavirus burden per million per day (select states)",
plotSub="Cases on main y-axis, deaths on secondary y-axis",
primYLab="Cases per million (7-day rolling mean)",
secYLab="Deaths per million (7-day rolling mean)",
facetFixed=TRUE,
popData=usafPrepped$usafDemo,
returnData=TRUE
)##
## Will scale by: 0.07901078
ne_casedeath## # A tibble: 2,124 x 5
## state date name vpm7 pop
## <chr> <date> <chr> <dbl> <dbl>
## 1 N NE 2020-03-03 CTP_cases NA 626042
## 2 N NE 2020-03-03 CTP_deaths NA 626042
## 3 N NE 2020-03-04 CTP_cases NA 1956650
## 4 N NE 2020-03-04 CTP_deaths NA 1956650
## 5 N NE 2020-03-05 CTP_cases NA 1956650
## 6 N NE 2020-03-05 CTP_deaths NA 1956650
## 7 N NE 2020-03-06 CTP_cases NA 1956650
## 8 N NE 2020-03-06 CTP_deaths NA 1956650
## 9 N NE 2020-03-07 CTP_cases NA 3285978
## 10 N NE 2020-03-07 CTP_deaths NA 3285978
## # ... with 2,114 more rowsAn attempt is made to align the curves for two different metrics in a single locale:
alignCurves <- function(df,
valueMetric,
depName,
indepName=setdiff(unique(df$name), depName),
lagsTry=0:30,
yLabel="Deaths per million",
depLabel="cases",
textMetric=stringr::str_split(yLabel, pattern=" ")[[1]][1] %>% stringr::str_to_lower()
) {
# FUNCTION ARGUMENTS
# df: a data frame containing state-date-name-valueMetric, with only 2 value types in 'name'
# valueMetric: the name of the value metric
# depName: the name of the dependent variable (the other will be the predictor)
# indepName: the name of the predictor variable
# lagsTry: the lagged values to attempt
# yLabel: label for the y-axis
# depLabel: label for the title (regression x-variable name)
# textMetric: label for the title (regression y-variable name)
# Check that there are only two values in column 'name'
if (length(unique(df$name))!=2) { stop("\nFunction depends on 'name' having only two possible values\n") }
# Arrange the data by state and date
df <- df %>%
arrange(state, date)
# Function to make a data frame with a specific lag
helperMakeLagData <- function(df, depName, indepName, valueMetric, lagValue) {
depData <- df %>%
filter(name==depName) %>%
select_at(vars(all_of(c("state", "date", valueMetric)))) %>%
purrr::set_names(c("state", "date", "depVar"))
indepData <- df %>%
filter(name==indepName) %>%
group_by(state) %>%
mutate(indepVar=lag(get(valueMetric), lagValue)) %>%
ungroup() %>%
select(state, date, indepVar)
fullData <- depData %>%
full_join(indepData, by=c("state", "date"))
fullData
}
# Run a simple linear model for depName ~ lag(otherName, lagsTry) to assess performance
lmResults <- vector("list", length(lagsTry))
n <- 1
for (lagValue in lagsTry) {
# Run the linear model with no intercept, save, and increment
lmResults[[n]] <- lm(depVar ~ indepVar:state + 0,
data=helperMakeLagData(df,
depName=depName,
indepName=indepName,
valueMetric=valueMetric,
lagValue=lagValue
)
)
n <- n + 1
}
# Find the best lag and coefficients
dfResults <- tibble::tibble(lags=lagsTry,
rsq=sapply(lmResults, FUN=function(x) summary(x)$r.squared)
)
p1 <- dfResults %>%
ggplot(aes(x=lags, y=rsq)) +
geom_point() +
labs(x="Lag", y="R-squared", title="R-squared vs. lag for aligning curves")
print(p1)
# Calculate the best lag and coefficients
bestLag <- dfResults %>%
filter(rsq==max(rsq)) %>%
pull(lags)
bestCoef <- coef(lmResults[[which(lagsTry==bestLag)]]) %>%
as.data.frame() %>%
purrr::set_names("mult") %>%
tibble::rownames_to_column("state") %>%
mutate(state=str_replace(state, "indepVar:state", ""))
# Plot the curves using the coefficients and lags
bestDF <- helperMakeLagData(df,
depName=depName,
indepName=indepName,
valueMetric=valueMetric,
lagValue=bestLag
) %>%
filter(!is.na(indepVar)) %>%
left_join(bestCoef, by="state") %>%
mutate(pred=mult*indepVar)
p2 <- bestDF %>%
select(state, date, depVar, pred, mult) %>%
pivot_longer(-c(state, date, mult)) %>%
mutate(name=case_when(name=="depVar" ~ "Actual value",
name=="pred" ~ "Predicted value\n(lag, mult)",
TRUE ~ "Unknown Element"
)
) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name, color=name)) +
geom_text(data=~filter(., date==max(date)),
aes(x=date, y=+Inf, label=paste0("Multiplier: ", round(mult, 3))),
hjust=1,
vjust=1
) +
labs(x="",
y=yLabel,
title=paste0("Predicting ",
textMetric,
" based on lagged ",
depLabel,
" (best lag: ",
bestLag,
" days)"
),
subtitle="Best lag is based on highest correlation/R-squared, common across all facets"
) +
facet_wrap(~state) +
scale_x_date(date_breaks="1 month", date_labels="%b") +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_discrete("Metric")
print(p2)
# Return the key data
list(bestLag=bestLag, bestCoef=bestCoef, bestDF=bestDF, lmResults=lmResults)
}
createAndAlignCurves <- function(df,
yVal,
namesPlot,
keyStates,
lagValueMetric,
lagDepName,
namesSec=NULL,
scaleSec=NULL,
plotTitle=NULL,
plotSub=NULL,
plotCaption=NULL,
primYLab=NULL,
secYLab="Caution, different metric and scale",
facetFixed=TRUE,
mapper=varMapper,
combStates=vector("character", 0),
popData=NULL,
yValPerCap = (yVal %in% c("vpm", "vpm7")),
printPlot=TRUE,
...
) {
# FUNCTION ARGUMENTS:
# df: data frame with integrated state data
# yVal: column to use for the yValues
# namesPlot: the values of column 'name' to be kept and plotted
# keyStates: states to be included
# if more than one state is passed, facets will be created
# lagValueMetric: the metric to be used for checking lags (typically 'vpm7')
# lagDepName: dependent variable (records in column 'name') to be used for the lagging process
# namesSec: names to be plotted on a secondary y-axes
# scaleSec: scale to be used for the secondary axis
# namesSec/scaleSec should be similar in magnitude to namesPlot
# plotTitle: plot title to be used (NULL means none)
# plotSub: plot subtitle to be used (NULL means none)
# plotCaption: plot caption to be used (NULL means none)
# primYLab: primary y label (NULL means use mapper)
# secYLab: secondary y label (default is "Caution, different metric and scale")
# facetFixed: boolean, if TRUE scales="fixed", if FALSE scales="free_y"
# only relevant if length(keyStates) > 1
# mapper: mapping file for variable names to descriptive names
# combStates: states that should be combined together for plotting (named vector, c("state"="newName"))
# popData: a population file for combining states
# yValPerCap: boolean, is the y-value of type per-capita?
# printPlot: boolean, whether to print the plots
# ...: other arguments to be passed to alignCurves()
# Create a frame to be used by the lagging process
tempMetrics <- plotStateMetric(df,
yVal=yVal,
namesPlot=namesPlot,
keyStates=keyStates,
namesSec=namesSec,
scaleSec=scaleSec,
plotTitle=plotTitle,
plotSub=plotSub,
plotCaption=plotCaption,
primYLab=primYLab,
secYLab=secYLab,
facetFixed=facetFixed,
mapper=mapper,
combStates=combStates,
popData=popData,
yValPerCap=yValPerCap,
printPlot=printPlot,
returnData=TRUE # the data must be returned for the next function
)
# Run the lagging process
tempLM <- alignCurves(tempMetrics, valueMetric=lagValueMetric, depName=lagDepName, ...)
# Return the key values
list(dfList=tempMetrics, lmList=tempLM)
}
# Example for northeastern states
neCurveList <- createAndAlignCurves(stateData,
yVal="vpm7",
namesPlot=c("CTP_cases"),
namesSec=c("CTP_deaths"),
keyStates=c("NY", "NJ", "MA", "CT", "RI", "NH", "VT", "ME", "DE", "DC"),
combStates=c("MA"="S NE", "CT"="S NE", "RI"="S NE",
"NH"="N NE", "VT"="N NE", "ME"="N NE",
"NY"="NY/NJ", "NJ"="NY/NJ",
"DE"="DE/DC", "DC"="DE/DC"
),
plotTitle="2020 coronavirus burden per million per day (select states)",
plotSub="Cases on main y-axis, deaths on secondary y-axis",
primYLab="Cases per million (7-day rolling mean)",
secYLab="Deaths per million (7-day rolling mean)",
facetFixed=TRUE,
popData=usafPrepped$usafDemo,
printPlot=TRUE,
lagValueMetric="vpm7",
lagDepName="CTP_deaths",
lagsTry=0:30
)##
## Will scale by: 0.07975869
## Warning: Removed 3 row(s) containing missing values (geom_path).
# Example for midwestern states
mwCurveList <- createAndAlignCurves(stateData,
yVal="vpm7",
namesPlot=c("CTP_cases"),
namesSec=c("CTP_deaths"),
keyStates=c("OH", "MI", "IN", "IL"),
plotTitle="2020 coronavirus burden per million per day (select states)",
plotSub="Cases on main y-axis, deaths on secondary y-axis",
primYLab="Cases per million (7-day rolling mean)",
secYLab="Deaths per million (7-day rolling mean)",
facetFixed=TRUE,
popData=usafPrepped$usafDemo,
printPlot=TRUE,
lagValueMetric="vpm7",
lagDepName="CTP_deaths",
lagsTry=0:30
)##
## Will scale by: 0.02321167
## Warning: Removed 3 row(s) containing missing values (geom_path).
The midwest is challenging to align. If using a single value for lag and a single value for CFR (case fatality rate), then predictions will have far too few deaths in the early months and far too many deaths in the later months.
The changes in CFR over time can also be estimated:
# Updated for automatic lag time assessment
assessStateCFR <- function(lst,
keyStates,
depVar,
indepVar,
depTitleName,
indepTitleName,
keyMetric="vpm7",
lagEarlyDate=as.Date("2020-03-31"),
lagMidDate=NULL,
lagLateDate=as.Date("2020-10-15"),
lagEarlyValue=10,
lagLateValue=20,
lagsTry=0:30
) {
# FUNCTION ARGUMENTS:
# lst: A list such as produced by createAndAlignCurves()
# keyStates: The key states to be extracted from the list
# depVar: the dependent variable
# indepVar: the independent variable
# depTitleName: the name for the dependent variable in the title
# indepTitleName: the name for the independent variable in the plot title
# keyMetric: the name of the key metric that is being assessed
# lagEarlyDate: the date for the earliest lagging calculation (dates before this will be at lagEarlyValue)
# lagMidDate: if lags are found from data, what midpoint should be used to split data as early vs late?
# NULL means midway between lagEarlyDate and lagLateDate
# lagLateDate: the date for the latest lagging calculation (dates after this will be at lagLateValue)
# lagEarlyValue: the value for lag on lagEarlyDate, will be linearly interpolated to lagLateValue/Date
# NULL means calculate from data and may differ by state
# lagLateValue: the value for lag on lagLateDate, will be linearly interpolated from lagEarlyValue/Date
# NULL means estimate from data and may differ by state
# lagsTry: the values for lag to be attempted if lageEarlyValue and/or lagLateValue is NULL
# Extract the data for keyStates
df <- lst[["dfList"]] %>%
filter(state %in% keyStates, !is.na(get(keyMetric))) %>%
pivot_wider(names_from="name", values_from=keyMetric)
# Function for finding lag time correlations
helperLagCor <- function(lt, lf, dp, id) {
lf %>%
group_by(state) %>%
mutate(y=get(dp), x=lag(get(id), lt)) %>%
summarize(p=cor(x, y, use="complete.obs"), .groups="drop_last") %>%
ungroup() %>%
mutate(lag=lt)
}
# Middle date for splitting data
if (is.null(lagMidDate)) lagMidDate <- mean(c(lagEarlyDate, lagLateDate))
# Get the early lags from the data
eLag <- map_dfr(.x=lagsTry, .f=helperLagCor, lf=filter(df, date<=lagMidDate), dp=depVar, id=indepVar) %>%
group_by(state) %>%
filter(p==max(p)) %>%
filter(row_number()==1) %>%
ungroup() %>%
select(state, earlyLag=lag)
# Get the late lags from the data
lLag <- map_dfr(.x=lagsTry, .f=helperLagCor, lf=filter(df, date>lagMidDate), dp=depVar, id=indepVar) %>%
group_by(state) %>%
filter(p==max(p)) %>%
filter(row_number()==1) %>%
ungroup() %>%
select(state, lateLag=lag)
# Create the full lag frame, including substituting the fixed value(s) if provided
lagFrame <- eLag %>%
inner_join(lLag, by="state")
if (!is.null(lagEarlyValue)) lagFrame <- lagFrame %>% mutate(earlyLag=lagEarlyValue)
if (!is.null(lagLateValue)) lagFrame <- lagFrame %>% mutate(lateLag=lagLateValue)
print(lagFrame)
# Apply the assumed lagging information
fullTime <- as.integer(lagLateDate-lagEarlyDate)
df <- df %>%
left_join(lagFrame, by="state") %>%
arrange(state, date) %>%
group_by(state) %>%
mutate(eLag=lag(get(indepVar), mean(earlyLag)),
lLag=lag(get(indepVar), mean(lateLag)),
pctEarly=pmin(pmax(as.integer(lagLateDate-date)/fullTime, 0), 1),
x=ifelse(is.na(eLag), NA, pctEarly*eLag + (1-pctEarly)*ifelse(is.na(lLag), 0, lLag)),
y=get(depVar),
mon=factor(month.abb[lubridate::month(date)], levels=month.abb)
) %>%
filter(!is.na(x)) %>%
ungroup()
# Regression for data from keyStates
if (length(keyStates) > 1) stateLM <- lm(y ~ x:mon:state + 0, data=df, na.action=na.exclude)
else stateLM <- lm(y ~ x:mon + 0, data=df, na.action=na.exclude)
# Add the predicted value to df
df <- df %>%
mutate(pred=predict(stateLM))
# Plot of curve overlaps
p1 <- df %>%
select(state, date, y, pred) %>%
pivot_longer(-c(state, date)) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(color=c("pred"="Predicted", "y"="Actual")[name], group=name)) +
scale_x_date(date_breaks="1 month", date_labels="%b") +
labs(x="",
y=stringr::str_to_title(depTitleName),
title=paste0("Predicted vs. actual ", depTitleName)
) +
scale_color_discrete("Metric") +
facet_wrap(~state)
print(p1)
# Plot of rate by month
p2 <- coef(stateLM) %>%
as.data.frame() %>%
purrr::set_names("CFR") %>%
tibble::rownames_to_column("monState") %>%
mutate(mon=factor(stringr::str_replace_all(monState, pattern="x:mon|:state.+", replacement=""),
levels=month.abb
),
state=if (length(keyStates)==1) keyStates
else stringr::str_replace_all(monState, pattern="x:mon[A-Za-z]{3}:state", replacement="")
) %>%
left_join(lagFrame, by="state") %>%
ggplot(aes(x=mon, y=CFR)) +
geom_col(fill="lightblue") +
geom_text(aes(y=CFR/2, label=paste0(round(100*CFR, 1), "%"))) +
geom_text(data=~filter(., mon==month.abb[lubridate::month(lagMidDate)]),
aes(x=-Inf, y=Inf, label=paste0("Early Lag: ", earlyLag)),
hjust=0,
vjust=1
) +
geom_text(data=~filter(., mon==month.abb[lubridate::month(lagMidDate)]),
aes(x=Inf, y=Inf, label=paste0("Late Lag: ", lateLag)),
hjust=1,
vjust=1
) +
labs(x="",
y=paste0(stringr::str_to_title(depTitleName), " as percentage of lagged ", indepTitleName),
title=paste0(stringr::str_to_title(depTitleName),
" vs. lagged ",
indepTitleName,
" in state(s): ",
paste0(keyStates, collapse=", ")
),
subtitle=paste0("Assumed early lag on ",
lagEarlyDate,
" interpolated to late lag on ",
lagLateDate
),
caption="Linear model coefficients on lagged data with no intercept used to estimate percentage"
) +
facet_wrap(~state)
print(p2)
# Return the data frame
df
}
# Deaths vs. cases in Michigan
mwOut <- assessStateCFR(mwCurveList,
keyStates=c("MI", "IL", "IN"),
depVar="CTP_deaths",
indepVar="CTP_cases",
depTitleName="deaths",
indepTitleName="cases",
lagEarlyValue=NULL,
lagLateValue=NULL
)## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(keyMetric)` instead of `keyMetric` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.## # A tibble: 3 x 3
## state earlyLag lateLag
## <chr> <int> <int>
## 1 IN 2 2
## 2 IL 5 3
## 3 MI 10 0
# Deaths vs. cases in NY/NJ and S NE
neOut <- assessStateCFR(neCurveList,
keyStates=c("NY/NJ", "S NE"),
depVar="CTP_deaths",
indepVar="CTP_cases",
depTitleName="deaths",
indepTitleName="cases",
lagEarlyValue=NULL,
lagLateValue=NULL
)## # A tibble: 2 x 3
## state earlyLag lateLag
## <chr> <int> <int>
## 1 NY/NJ 6 24
## 2 S NE 6 0
## Warning: Removed 4 rows containing missing values (position_stack).## Warning: Removed 4 rows containing missing values (geom_text).
The CFR declines in more recent months, possibly as a function of a greater number of tests finding less serious disease. Lag times are variable but typically seem to be around a week. The use of both lag times and variable CFR by month introduces some risk of over-fitting.
The process for investigating lags and leads can also be refreshed with new data:
lagVectorWindows <- function(v1,
v2,
lagsTry=0:30,
windowSize=30,
minNoNA=ceiling(windowSize/2),
updateStatus=FALSE,
isLag=TRUE
) {
# FUNCTION ARGUMENTS:
# v1: the first vector, which will be used 'as is'
# v2: the second vector, which will be lagged/led by various values for lagsTry
# lagsTry: the values for x that will be used in cor(v1, lag/lead(v2, x))
# windowSize: the size of the window to use in taking snippets of v1 and lagged/led v2
# minNoNA: minimum number of non-NA lagged/led values needed to calculate a correlation
# updateStates: whether to print which window is being worked on
# isLag: boolean, should a lag or a lead be applied (TRUE is lag, FALSE is lead)
# Find the function to be used
func <- if (isLag) lag else lead
# Find the list of all possible window start points
windowStarts <- 1:(length(v1)-windowSize+1)
# Helper function to create a frame of correlations
helperCorr <- function(s) {
# Create the end point for the vector
e <- s + windowSize - 1
# Announce the start
if (updateStatus) cat("\nProcessing window starting at:", s)
# Create the correlations tibble for all values of lag, and return
tibble::tibble(startWindow=s, endWindow=e, lags=lagsTry) %>%
mutate(na1=sum(is.na(v1[s:e])),
na2=sapply(lags, FUN=function(x) sum(is.na(func(v2, x)[s:e]))),
p=sapply(lags,
FUN=function(x) {
ifelse(sum(!is.na(func(v2, x)[s:e])) < minNoNA,
NA,
cor(v1[s:e], func(v2, x)[s:e], use="complete.obs")
)
}
)
)
}
# Bind the correlations frames and return
map_dfr(windowStarts, .f=helperCorr)
}
# Function to assess correlations by lag/lead and window by state
stateCorr <- function(lst,
keyState,
met="vpm7",
v1Name="CTP_deaths",
v2Name="CTP_cases",
windowSize=42,
isLag=TRUE
) {
# FUNCTION ARGUMENTS:
# lst: the processed list
# keyState: the state of interest
# met: the metric of interest
# v1Name: the name of the first vector (this is considered fixed)
# v2Name: the name of the second vector (this will have the lead/lag applied to it)
# windowSize: number of days in the window
# isLag: boolean, whether to use lag (TRUE) or lead (FALSE) on v2Name
# Extract the data for the key State
df <- lst[["dfList"]] %>%
filter(state %in% keyState, !is.na(get(met))) %>%
arrange(state, date, name)
# Get the minimum date that is common to both
minDate <- df %>%
group_by(name) %>%
summarize(date=min(date), .groups="drop_last") %>%
pull(date) %>%
max()
# Extract v1 and v2
v1 <- df %>%
filter(name==v1Name, date>=minDate) %>%
pull(met)
v2 <- df %>%
filter(name==v2Name, date>=minDate) %>%
pull(met)
# Confirm that dates are the same for both vectors
dfDates1 <- df %>% filter(name==v1Name, date>=minDate) %>% pull(date)
dfDates2 <- df %>% filter(name==v2Name, date>=minDate) %>% pull(date)
if (!all.equal(dfDates1, dfDates2)) stop("\nDate mismatch\n")
# Find the lags in the data
dfLags <- lagVectorWindows(v1, v2, lagsTry=0:30, windowSize=windowSize, isLag=isLag) %>%
mutate(windowStartDate=dfDates1[startWindow])
# Give the description of the lag or lead
descr <- ifelse(isLag, "lag (days)", "lead (days)")
# Boxplot of correlations by lag
p1 <- dfLags %>%
filter(!is.na(p)) %>%
ggplot(aes(x=factor(lags), y=p)) +
geom_boxplot(fill="lightblue") +
labs(x=stringr::str_to_title(descr),
y="Correlation",
title=paste0("Box plot of correlation by ", descr)
)
print(p1)
# Plot of best lags by starting date
p2 <- dfLags %>%
filter(!is.na(p)) %>%
group_by(startWindow) %>%
filter(p==max(p)) %>%
ggplot(aes(x=windowStartDate, y=lags)) +
geom_point(aes(size=p)) +
labs(x="Window start date",
y=paste0("Best ", descr),
title=paste0("Best ", descr, " by window starting date")
) +
scale_size_continuous(paste0("p at best ", stringr::str_replace(descr, " .*", ""))) +
scale_x_date(date_breaks="1 month", date_labels="%b")
print(p2)
# Plot of correlations by lag
p3 <- dfLags %>%
filter(!is.na(p)) %>%
ggplot(aes(x=windowStartDate, y=lags)) +
geom_tile(aes(fill=p)) +
labs(x="Window start date",
y=stringr::str_to_title(descr),
title=paste0(stringr::str_to_title(descr), " by window starting date")
) +
scale_color_continuous(paste0("p at ", stringr::str_replace(descr, " .*", ""))) +
scale_x_date(date_breaks="1 month", date_labels="%b")
print(p3)
# Rename variable lags to leads if isLag is FALSE
if (isFALSE(isLag)) dfLags <- dfLags %>%
rename(leads=lags)
# Return dfLags
dfLags
}
miLeadData <- stateCorr(mwCurveList, keyState="MI", v1Name="CTP_cases", v2Name="CTP_deaths", isLag=FALSE)
nynjLeadData <- stateCorr(neCurveList, keyState="NY/NJ", v1Name="CTP_cases", v2Name="CTP_deaths", isLag=FALSE)
And the full process can be run for the state of Texas:
# Example for southern states
soCurveList <- createAndAlignCurves(stateData,
yVal="vpm7",
namesPlot=c("CTP_cases"),
namesSec=c("CTP_deaths"),
keyStates=c("AZ", "FL", "GA", "TX"),
plotTitle="2020 coronavirus burden per million per day (select states)",
plotSub="Cases on main y-axis, deaths on secondary y-axis",
primYLab="Cases per million (7-day rolling mean)",
secYLab="Deaths per million (7-day rolling mean)",
facetFixed=TRUE,
popData=usafPrepped$usafDemo,
printPlot=TRUE,
lagValueMetric="vpm7",
lagDepName="CTP_deaths",
lagsTry=0:30
)##
## Will scale by: 0.02108297
## Warning: Removed 3 row(s) containing missing values (geom_path).
assessStateCFR(soCurveList,
keyStates=c("AZ", "FL", "GA", "TX"),
depVar="CTP_deaths",
indepVar="CTP_cases",
depTitleName="deaths",
indepTitleName="cases"
)## # A tibble: 4 x 3
## state earlyLag lateLag
## <chr> <dbl> <dbl>
## 1 GA 10 20
## 2 TX 10 20
## 3 AZ 10 20
## 4 FL 10 20
## Warning: Removed 4 rows containing missing values (position_stack).## Warning: Removed 4 rows containing missing values (geom_text).
## # A tibble: 967 x 13
## state date CTP_cases CTP_deaths earlyLag lateLag eLag lLag pctEarly
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AZ 2020-03-17 1.17 0 10 20 0.0837 NA 1
## 2 AZ 2020-03-18 1.92 0.0209 10 20 0.146 NA 1
## 3 AZ 2020-03-19 2.93 0.0418 10 20 0.146 NA 1
## 4 AZ 2020-03-20 5.17 0.0418 10 20 0.126 NA 1
## 5 AZ 2020-03-21 7.05 0.105 10 20 0.146 NA 1
## 6 AZ 2020-03-22 8.83 0.126 10 20 0.146 NA 1
## 7 AZ 2020-03-23 11.2 0.167 10 20 0.272 NA 1
## 8 AZ 2020-03-24 14.0 0.272 10 20 0.293 NA 1
## 9 AZ 2020-03-25 16.1 0.293 10 20 0.398 NA 1
## 10 AZ 2020-03-26 16.0 0.314 10 20 0.732 NA 1
## # ... with 957 more rows, and 4 more variables: x <dbl>, y <dbl>, mon <fct>,
## # pred <dbl>txLeadData <- stateCorr(soCurveList, keyState="TX", v1Name="CTP_cases", v2Name="CTP_deaths", isLag=FALSE)
The process for assessing integrated data is converted to a main function:
integrateStateData <- function(stateData=NULL,
popData=NULL,
ctpList=NULL,
usafData=NULL,
cdcList=NULL,
glimpseStateData=NULL,
runAll=FALSE,
runTwoAxis=runAll,
yVal="vpm7",
var1="CTP_cases",
var2="CTP_deaths",
keyStates=sort(c(state.abb, "DC")),
combStates=vector("character", 0),
mapper=varMapper,
runCreateAlign=runAll,
lagsTry=0:30,
runCFR=runAll,
lagEarlyValue=10,
lagLateValue=20,
lagEarlyDate=as.Date("2020-03-31"),
lagMidDate=NULL,
lagLateDate=as.Date("2020-10-15")
) {
# FUNCTION ARGUMENTS:
# stateData: an integrated state-level data file (NULL means create from components)
# popData: a state-level population data file (must be passed if stateData is not NULL)
# ctpList: a processed list of COVID Tracking Project data (must be provided if stateData=NULL)
# usafData: a processed tibble of USA Facts data (must be provided if stateData=NULL)
# cdcList: a processed list of CDC All-Cause deaths data (must be provided if stateData=NULL)
# glimpseStateData: boolean, whether to glimpse the stateData file (NULL means only if from components)
# runAll: boolean, whether to set al of runTwoAxis, runCreateAlign, and runCFR all to the same value
# runTwoAxis: whether to show a plot of two metrics on two axes
# yVal: the y-value to use from stateData
# var1: the first of the two variables of interest (should be the leading component if a drives b)
# var2: the second of the two variables of interest (should be the lagging component if a drives b)
# keyStates: subset of states to use for analysis
# combStates: states that should be combined together for plotting (named vector, c("state"="newName"))
# mapper: mapping file for variables to descriptive names
# runCreateAlign: boolean, should createAndAlignCurves() be run?
# lagsTry: lag values to try (applies to both createAlignCurves and assessStateCFR)
# runCFR: boolean, should assessStateCFR be run?
# lagEarlyValue: the value for lag on lagEarlyDate, will be linearly interpolated to lagLateValue/Date
# NULL means calculate from data and may differ by state
# lagLateValue: the value for lag on lagLateDate, will be linearly interpolated from lagEarlyValue/Date
# NULL means estimate from data and may differ by state
# lagEarlyDate: the date for the earliest lagging calculation (dates before this will be at lagEarlyValue)
# lagMidDate: if lags are found from data, what midpoint should be used to split data as early vs late?
# NULL means midway between lagEarlyDate and lagLateDate
# lagLateDate: the date for the latest lagging calculation (dates after this will be at lagLateValue)
# Check that either stateData or its components have been provided
if (!is.null(stateData)) {
cat("\nA file has been passed for stateData, components will be ignored\n")
if (is.null(popData)) stop("Must also pass a popData file of state-level population\n")
if (is.null(glimpseStateData)) glimpseStateData <- FALSE
} else {
if (is.null(ctpList) | is.null(usafData) | is.null(cdcList)) {
stop("\nMust provided all of ctpList, usafData, cdcList when stateData is NULL\n")
}
cat("\nBuilding stateData from the passed components\n")
if (is.null(glimpseStateData)) glimpseStateData <- TRUE
# Create COVID Tracking Project File
ctpPrepped <- prepCTPData(ctpList)
# Create USA Facts file
usafPrepped <- prepUSAFData(usafData)
# Create an integrated state population demographics file
demoData <- ctpPrepped$ctpDemo %>%
rename(popCTP=pop) %>%
full_join(rename(usafPrepped$usafDemo, popUSAF=pop), by="state") %>%
mutate(pop=pmax(popCTP, popUSAF))
# Create CDC All-Cause File
cdcPrepped <- prepCDCData(cdcList, popData=demoData)
# Integrated state data
stateData <- ctpPrepped$ctpData %>%
bind_rows(usafPrepped$usafData) %>%
bind_rows(cdcPrepped$cdcData)
# Create popData if not provided
if (is.null(popData)) popData <- demoData %>% select(state, pop)
}
# Show summaries of stateData if requested
if (glimpseStateData) {
# Glimpse the file
glimpse(stateData)
# Show control totals
stateData %>%
group_by(name) %>%
summarize(value=sum(value, na.rm=TRUE), value7=sum(value7, na.rm=TRUE), .groups="drop_last") %>%
print()
}
# Run plotStateMetric if requested
if (runTwoAxis) {
caseDeath <- plotStateMetric(stateData,
yVal=yVal,
namesPlot=var1,
namesSec=var2,
keyStates=keyStates,
combStates=combStates,
plotTitle="2020 coronavirus burden per million per day (select states)",
plotSub="Caution that metrics are on different axes and scales",
primYLab=paste0(mapper[var1], "\n", mapper[yVal]),
secYLab=paste0(mapper[var2], "\n", mapper[yVal]),
facetFixed=TRUE,
popData=popData,
returnData=TRUE
)
} else {
caseDeath <- NULL
}
# Run createAndAlignCurves() if requested
if (runCreateAlign) {
curveList <- createAndAlignCurves(stateData,
yVal=yVal,
namesPlot=var1,
namesSec=var2,
keyStates=keyStates,
combStates=combStates,
plotTitle="2020 coronavirus burden per million per day (select states)",
plotSub="Caution that metrics are on different axes and scales",
primYLab=paste0(mapper[var1], "\n", mapper[yVal]),
secYLab=paste0(mapper[var2], "\n", mapper[yVal]),
facetFixed=TRUE,
popData=popData,
printPlot=TRUE,
lagValueMetric=yVal,
lagDepName=var2,
lagsTry=lagsTry
)
} else {
curveList <- NULL
}
# Run assessStateCFR() if requested (requires that curveList have been created)
if (runCFR & is.null(curveList)) {
cat("\nassessStateCFR requires runCreateAlign=TRUE; skipping and setting runCFR=FALSE\n")
runCFR <- FALSE
}
if (runCFR) {
# Create the list of key states based on keyStates and combStates
useStates <- keyStates
for (ctr in 1:length(useStates)) {
if (useStates[ctr] %in% names(combStates)) useStates[ctr] <- combStates[useStates[ctr]]
}
useStates <- unique(useStates)
# Run assessStateCFR
cfrList <- assessStateCFR(curveList,
keyStates=useStates,
depVar=var2,
indepVar=var1,
depTitleName=stringr::str_replace(var2, ".*_", ""),
indepTitleName=stringr::str_replace(var1, ".*_", ""),
keyMetric=yVal,
lagEarlyDate=lagEarlyDate,
lagMidDate=lagMidDate,
lagLateDate=lagLateDate,
lagEarlyValue=lagEarlyValue,
lagLateValue=lagLateValue,
lagsTry=lagsTry
)
} else {
cfrList <- NULL
}
# Return a list of the key data
list(stateData=stateData, popData=popData, caseDeath=caseDeath, curveList=curveList, cfrList=cfrList)
}
# Example for creating a full stateData file
fullStateList <- integrateStateData(ctpList=readFromRDS("test_old_201108"),
usafData=readFromRDS("cty_old_20201109")$clusterStateData,
cdcList=readFromRDS("cdcList_20201110")
)##
## Building stateData from the passed components
## Rows: 104,103
## Columns: 9
## $ state <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", ...
## $ date <date> 2020-03-06, 2020-03-07, 2020-03-08, 2020-03-09, 2020-03-10,...
## $ metric <chr> "cases", "cases", "cases", "cases", "cases", "cases", "cases...
## $ source <chr> "CTP", "CTP", "CTP", "CTP", "CTP", "CTP", "CTP", "CTP", "CTP...
## $ name <chr> "CTP_cases", "CTP_cases", "CTP_cases", "CTP_cases", "CTP_cas...
## $ value <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 3, 0, 6, 2, 8, 0, 14, 6,...
## $ value7 <dbl> NA, NA, NA, 0.0000000, 0.1428571, 0.1428571, 0.1428571, 0.14...
## $ vpm <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, ...
## $ vpm7 <dbl> NA, NA, NA, 0.0000000, 0.1934601, 0.1934601, 0.1934601, 0.19...
## # A tibble: 8 x 3
## name value value7
## <chr> <dbl> <dbl>
## 1 CDC_deaths 2224832. 2173735.
## 2 CDC_excess 242466. 240886.
## 3 CTP_cases 9717214 9372412.
## 4 CTP_deaths 228262 224869.
## 5 CTP_hosp 9301372 9036822.
## 6 CTP_tests 154892254 150735326.
## 7 USAF_cases 9679664 9336904.
## 8 USAF_deaths 233286 230182.fullStateList## $stateData
## # A tibble: 104,103 x 9
## state date metric source name value value7 vpm vpm7
## <chr> <date> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 AK 2020-03-06 cases CTP CTP_cases 0 NA 0 NA
## 2 AK 2020-03-07 cases CTP CTP_cases 0 NA 0 NA
## 3 AK 2020-03-08 cases CTP CTP_cases 0 NA 0 NA
## 4 AK 2020-03-09 cases CTP CTP_cases 0 0 0 0
## 5 AK 2020-03-10 cases CTP CTP_cases 0 0.143 0 0.193
## 6 AK 2020-03-11 cases CTP CTP_cases 0 0.143 0 0.193
## 7 AK 2020-03-12 cases CTP CTP_cases 0 0.143 0 0.193
## 8 AK 2020-03-13 cases CTP CTP_cases 1 0.143 1.35 0.193
## 9 AK 2020-03-14 cases CTP CTP_cases 0 0.429 0 0.580
## 10 AK 2020-03-15 cases CTP CTP_cases 0 0.857 0 1.16
## # ... with 104,093 more rows
##
## $popData
## # A tibble: 51 x 2
## state pop
## <chr> <dbl>
## 1 AK 738432
## 2 AL 4858979
## 3 AR 2978204
## 4 AZ 6828065
## 5 CA 39144818
## 6 CO 5456574
## 7 CT 3590886
## 8 DC 672228
## 9 DE 945934
## 10 FL 20271272
## # ... with 41 more rows
##
## $caseDeath
## NULL
##
## $curveList
## NULL
##
## $cfrList
## NULL# Example for using the full stateData file
nenynjList <- integrateStateData(stateData=fullStateList$stateData,
popData=fullStateList$popData,
runAll=TRUE,
keyStates=c("NY", "NJ", "MA", "CT", "RI", "NH", "VT", "ME"),
combStates=c("MA"="S NE", "CT"="S NE", "RI"="S NE",
"NH"="N NE", "VT"="N NE", "ME"="N NE"
),
lagEarlyValue=NULL,
lagLateValue=NULL
)##
## A file has been passed for stateData, components will be ignored
##
## Will scale by: 0.07901078
##
## Will scale by: 0.07901078
## Warning: Removed 3 row(s) containing missing values (geom_path).
## # A tibble: 4 x 3
## state earlyLag lateLag
## <chr> <int> <int>
## 1 N NE 3 27
## 2 NY 6 17
## 3 S NE 6 0
## 4 NJ 8 30
## Warning: Removed 8 rows containing missing values (position_stack).## Warning: Removed 8 rows containing missing values (geom_text).
# Example for alignment between USA Facts and CTP
testList <- integrateStateData(stateData=fullStateList$stateData,
popData=fullStateList$popData,
runAll=TRUE,
keyStates=c("NY", "NJ", "MA", "CT", "RI", "NH", "VT", "ME"),
combStates=c("MA"="S NE", "CT"="S NE", "RI"="S NE",
"NH"="N NE", "VT"="N NE", "ME"="N NE"
),
var1="CTP_deaths",
var2="USAF_deaths",
lagEarlyValue=NULL,
lagLateValue=NULL
)##
## A file has been passed for stateData, components will be ignored
##
## Will scale by: 1.281759
##
## Will scale by: 1.281759
## Warning: Removed 2 row(s) containing missing values (geom_path).
## # A tibble: 4 x 3
## state earlyLag lateLag
## <chr> <int> <int>
## 1 N NE 0 0
## 2 NJ 0 0
## 3 S NE 1 2
## 4 NY 2 4
## Warning: Removed 8 rows containing missing values (position_stack).
## Warning: Removed 8 rows containing missing values (geom_text).
The differences in data reporting between CTP and USA Facts stand out. Lags are generally zero, though the spikes mean that each source has a different cumulative number of deaths. The process can similarly be run for the Great Lakes states (excluding Pennsylvania and New York):
# Example for using the full stateData file
glakesList <- integrateStateData(stateData=fullStateList$stateData,
popData=fullStateList$popData,
runAll=TRUE,
keyStates=c("MN", "WI", "IL", "IN", "MI", "OH"),
lagEarlyValue=NULL,
lagLateValue=NULL
)##
## A file has been passed for stateData, components will be ignored
##
## Will scale by: 0.01614313
##
## Will scale by: 0.01614313
## Warning: Removed 3 row(s) containing missing values (geom_path).
## # A tibble: 6 x 3
## state earlyLag lateLag
## <chr> <int> <int>
## 1 IN 2 2
## 2 MN 3 13
## 3 WI 3 30
## 4 IL 5 3
## 5 MI 10 0
## 6 OH 11 19
Creating separate lags by state as well as separate CFR by month is important in the Great Lakes. Michigan had a very large spike early when there was little case-death lag and a high CFR. By contrast, Wisconsin had a large late spike when there was more meaningful case-death lag and a much lower CFR. Indiana especially needs the variation by month as its early spike had ~6% CFR and its late spike had ~1% CFR.
Functions have been updated so that .groups=“drop_last” in every summarize() call to avoid the note about dplyr default behavior. Further, the lm() has been updated to na.action=na.exclude so that predict() will return NA for all rows with missing data (required for hospital data which has variable date of entry by state).
Suppose that the goal is to check how hospitalizations and deaths correlate:
# Example for using the full stateData file
glakesHospList <- integrateStateData(stateData=fullStateList$stateData,
popData=fullStateList$popData,
runAll=TRUE,
keyStates=c("MN", "WI", "IL", "IN", "MI", "OH"),
var1="CTP_hosp",
var2="CTP_deaths",
lagEarlyValue=NULL,
lagLateValue=NULL
)##
## A file has been passed for stateData, components will be ignored
##
## Will scale by: 0.04114457
##
## Will scale by: 0.04114457
## Warning: Removed 3 row(s) containing missing values (geom_path).
## # A tibble: 6 x 3
## state earlyLag lateLag
## <chr> <int> <int>
## 1 MN 0 11
## 2 MI 3 5
## 3 WI 3 15
## 4 IN 5 3
## 5 OH 9 24
## 6 IL 10 0
## Warning: Removed 7 rows containing missing values (position_stack).## Warning: Removed 7 rows containing missing values (geom_text).
Even with total hospitalized being a state variable rather than a flow variable, it appears to be a better predicor of changes in deaths in the next week or so. Specifically, even when using a common 7-day lag across all states and dates, application of a single multiplier in the 2%-3% range by state appears to drive reasonably good convergence of the curves. It is a much more stable predictor over time than cases.
Suppose that the goal is to assess whether increases in CDC all-cause deaths link to increases in coronavirus deaths:
# Example for using the full stateData file
glakesCDC1List <- integrateStateData(stateData=fullStateList$stateData,
popData=fullStateList$popData,
runAll=TRUE,
keyStates=c("MN", "WI", "IL", "IN", "MI", "OH"),
var1="CDC_excess",
var2="CTP_deaths",
lagEarlyValue=NULL,
lagLateValue=NULL
)##
## A file has been passed for stateData, components will be ignored
##
## Will scale by: 0.7824127
##
## Will scale by: 0.7824127
## Warning: Removed 59 row(s) containing missing values (geom_path).
## # A tibble: 6 x 3
## state earlyLag lateLag
## <chr> <int> <int>
## 1 MN 0 0
## 2 OH 1 2
## 3 MI 3 7
## 4 IN 5 18
## 5 WI 7 4
## 6 IL 8 0## Warning: Removed 110 row(s) containing missing values (geom_path).
# Example for using the full stateData file
glakesCDC2List <- integrateStateData(stateData=fullStateList$stateData,
popData=fullStateList$popData,
runAll=TRUE,
keyStates=c("MN", "WI", "IL", "IN", "MI", "OH"),
var1="CTP_deaths",
var2="CDC_excess",
lagEarlyValue=NULL,
lagLateValue=NULL
)##
## A file has been passed for stateData, components will be ignored
##
## Will scale by: 1.278098
##
## Will scale by: 1.278098
## Warning: Removed 63 row(s) containing missing values (geom_path).
## # A tibble: 6 x 3
## state earlyLag lateLag
## <chr> <int> <int>
## 1 IL 0 0
## 2 IN 0 26
## 3 MI 0 12
## 4 MN 0 4
## 5 OH 0 23
## 6 WI 0 0## Warning: Removed 126 row(s) containing missing values (geom_path).
The link between CDC excess all-cause deaths and reported coronavirus deaths is less clear. There appear to be roughly 30% more excess all-cause deaths than reported coronavirus deaths, and with the excess all-cause deaths sometimes leading the reported coronavirus deaths (possibly due to differences in how death date is tracked). Further, Wisconsin did not have excess deaths until Fall, so the predictive nature of this metric is poor in Wisconsin.
Do increases in coronavirus cases predict increases in CDC all-cause deaths?
# Example for using the full stateData file
glakesCDC3List <- integrateStateData(stateData=fullStateList$stateData,
popData=fullStateList$popData,
runAll=TRUE,
keyStates=c("MN", "WI", "IL", "IN", "MI", "OH"),
var1="CTP_cases",
var2="CDC_excess",
lagEarlyValue=NULL,
lagLateValue=NULL
)##
## A file has been passed for stateData, components will be ignored
##
## Will scale by: 0.0206325
##
## Will scale by: 0.0206325
## Warning: Removed 66 row(s) containing missing values (geom_path).
## # A tibble: 6 x 3
## state earlyLag lateLag
## <chr> <int> <int>
## 1 IL 0 9
## 2 IN 0 13
## 3 MN 0 15
## 4 OH 0 29
## 5 WI 0 5
## 6 MI 7 16## Warning: Removed 126 row(s) containing missing values (geom_path).
Since excess deaths and coronavirus deaths are reasonably correlated, increases in cases help predict increases in excess all-cause deaths. The rate of excess death per case decreases significantly with time.